The Coronavirus Dashboard
This Coronavirus dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. This dashboard is built with R using the Rmakrdown framework and can easily reproduce by others. The code behind the dashboard available here
Data
The input data for this dashboard is the coronavirus R package (dev version). The data and dashboard is refreshed on a daily bases. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository
Packages
Deployment and reproducibly
The dashboard was deployed to Github docs. If you wish to deploy and/or modify the dashboard on your Github account, you can apply the following steps:
For any question or feedback, you can either open an issue or contact me on Twitter.
---
title: "Coronavirus Dashboard"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
vertical_layout: fill
---
```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- read.csv("https://raw.githubusercontent.com/RamiKrispin/coronavirus/master/csv/coronavirus.csv", stringsAsFactors = FALSE) %>%
dplyr::mutate(country = ifelse(country == "United Arab Emirates", "UAE", country),
country = ifelse(country == "Mainland China", "China", country),
country = ifelse(country == "North Macedonia", "N.Macedonia", country),
country = trimws(country),
country = factor(country, levels = unique(country)))
df_daily <- df %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE),
.groups = "drop") %>%
tidyr::pivot_wider(names_from = type,
values_from = total) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(active = confirmed - death - recovered) %>%
dplyr::mutate(confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
recovered_cum = cumsum(recovered),
active_cum = cumsum(active))
df_tree <- df %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total = sum(cases), .groups = "drop") %>%
dplyr::mutate(type = ifelse(type == "confirmed", "Confirmed", type),
type = ifelse(type == "recovered", "Recovered", type),
type = ifelse(type == "death", "Death", type)) %>%
tidyr::pivot_wider(names_from = type, values_from = total) %>%
dplyr::mutate(Active = Confirmed - Death - Recovered) %>%
tidyr::pivot_longer(cols = -country, names_to = "type", values_to = "total")
df_world <- df_tree %>%
dplyr::group_by(type) %>%
dplyr::summarise(total = sum(total), .groups = "drop") %>%
tidyr::pivot_wider(names_from = type, values_from = total)
names(df_world) <- tolower(names(df_world))
```
Summary
=======================================================================
Row
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(df_world$confirmed, big.mark = ","), "", sep = " "),
caption = "Total Confirmed Cases",
icon = "fas fa-user-md",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(df_world$active[1], big.mark = ","), " (",
round(100 * df_world$active[1] / df_world$confirmed[1], 1),
"%)", sep = ""),
caption = "Active Cases", icon = "fas fa-ambulance",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(df_world$recovered[1] , big.mark = ","), " (",
round(100 * df_world$recovered[1] / df_world$confirmed[1], 1),
"%)", sep = ""),
caption = "Recovered Cases", icon = "fas fa-heartbeat",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(df_world$death[1] , big.mark = ","), " (",
round(100 * df_world$death[1] / df_world$confirmed[1], 1),
"%)", sep = ""),
caption = "Death Cases",
icon = "fas fa-heart-broken",
color = death_color)
```
Row {.tabset}
-----------------------------------------------------------------------
### Cases Distribution by Type (`r max(df$date)`)
```{r daily_summary}
plotly::plot_ly(
data = df_tree %>% dplyr::filter(type == "Confirmed"),
type= "treemap",
values = ~total,
labels= ~ country,
parents= ~type,
domain = list(column=0),
name = "Confirmed",
textinfo="label+value+percent parent"
) %>%
plotly::add_trace(
data = df_tree %>% dplyr::filter(type == "Active"),
type= "treemap",
values = ~total,
labels= ~ country,
parents= ~type,
domain = list(column=1),
name = "Active",
textinfo="label+value+percent parent"
) %>%
plotly::add_trace(
data = df_tree %>% dplyr::filter(type == "Recovered"),
type= "treemap",
values = ~total,
labels= ~ country,
parents= ~type,
domain = list(column=2),
name = "Recovered",
textinfo="label+value+percent parent"
) %>%
plotly::add_trace(
data = df_tree %>% dplyr::filter(type == "Death"),
type= "treemap",
values = ~total,
labels= ~ country,
parents= ~type,
domain = list(column=3),
name = "Death",
textinfo="label+value+percent parent"
) %>%
plotly::layout(grid=list(columns=4, rows=1))
```
### Daily Cumulative Cases
```{r}
plotly::plot_ly(data = df_daily,
x = ~ date,
y = ~ active_cum,
name = 'Active',
fillcolor = active_color,
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
plotly::add_trace(y = ~ recovered_cum,
name = "Recovered",
fillcolor = recovered_color) %>%
plotly::add_trace(y = ~ death_cum,
name = "Death",
fillcolor = death_color) %>%
plotly::layout(title = "",
yaxis = list(title = "Cumulative Number of Cases"),
xaxis = list(title = "Date",
type = "date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare")
```
### Recovery/Death Ratio
```{r}
df %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total_cases = sum(cases)) %>%
tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
dplyr::arrange(- confirmed) %>%
dplyr::filter(confirmed >= 20000) %>%
dplyr::mutate(recover_rate = recovered / confirmed,
death_rate = death / confirmed) %>%
dplyr::mutate(recover_rate = dplyr::if_else(is.na(recover_rate), 0, recover_rate),
death_rate = dplyr::if_else(is.na(death_rate), 0, death_rate)) %>%
dplyr::ungroup() %>%
dplyr::mutate(confirmed_normal = as.numeric(confirmed) / max(as.numeric(confirmed))) %>%
plotly::plot_ly(y = ~ round(100 * recover_rate, 1),
x = ~ round(100 * death_rate, 1),
size = ~ log(confirmed),
sizes = c(5, 70),
type = 'scatter', mode = 'markers',
color = ~ country,
marker = list(sizemode = 'diameter' , opacity = 0.5),
hoverinfo = 'text',
text = ~paste("", country,
" Confirmed Cases: ", confirmed,
" Recovery Rate: ", paste(round(100 * recover_rate, 1), "%", sep = ""),
" Death Rate: ", paste(round(100 * death_rate, 1), "%", sep = ""))
) %>%
plotly::layout(title = "Recovery / Death Ratio (Countries with More than 20,000 Cases)",
yaxis = list(title = "Recovery Rate", ticksuffix = "%"),
xaxis = list(title = "Death Rate", ticksuffix = "%",
dtick = 1,
tick0 = 0),
hovermode = "compare")
```
### Map
```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(dplyr)
library(purrr)
cv_data_for_plot <- df %>%
filter(cases > 0) %>%
group_by(country,province,lat,long,type) %>%
summarise(cases = sum(cases)) %>%
mutate(log_cases = 2 * log(cases)) %>%
ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red","green"), domain = c("confirmed", "death","recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
purrr::walk( function(df) {
map_object <<- map_object %>%
addCircleMarkers(data=cv_data_for_plot.split[[df]],
lng=~long, lat=~lat,
# label=~as.character(cases),
color = ~pal(type),
stroke = FALSE,
fillOpacity = 0.8,
radius = ~log_cases,
popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
feature.id = FALSE,
row.numbers = FALSE,
zcol=c("type","cases","country","province")),
group = df,
# clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
labelOptions = labelOptions(noHide = F,
direction = 'auto'))
})
map_object %>%
addLayersControl(
overlayGroups = names(cv_data_for_plot.split),
options = layersControlOptions(collapsed = FALSE)
)
```
### Data
```{r}
df_rates <- df_tree %>%
dplyr::filter(type != "Active") %>%
tidyr::pivot_wider(names_from = "type", values_from = "total") %>%
dplyr::mutate(recovery_rate = Recovered / Confirmed,
death_rate = Death / Confirmed)
bar_chart <- function(label, width = "100%", height = "14px", fill = "#00bfc4", background = NULL) {
bar <- htmltools::div(style = list(background = fill, width = width, height = height))
chart <- htmltools::div(style = list(flexGrow = 1, marginLeft = "6px", background = background), bar)
htmltools::div(style = list(display = "flex", alignItems = "center"), label, chart)
}
reactable::reactable(df_rates,
pagination = FALSE,
highlight = TRUE,
height = 800,
sortable = FALSE,
borderless = TRUE,
defaultPageSize = nrow(df_rates),
columns = list(
country = reactable::colDef(name = "Country", minWidth = 50, maxWidth = 100),
Confirmed = reactable::colDef(name = "Confirmed", minWidth = 50, maxWidth = 80),
Recovered = reactable::colDef(name = "Recovered", minWidth = 50, maxWidth = 100),
Death = reactable::colDef(name = "Death", minWidth = 50, maxWidth = 100),
recovery_rate = reactable::colDef(name = "Recovery Rate", minWidth = 50, maxWidth = 100,
cell = function(value) {
width <- paste0(value , "%")
# Add thousands separators
value <- format(value, big.mark = ",")
bar_chart(value, width = width, fill = "green")
},
align = "left"),
death_rate = reactable::colDef(name = "Death Rate", minWidth = 50, maxWidth = 100,
cell = function(value) {
width <- paste0(value , "%")
# Add thousands separators
value <- format(value, big.mark = ",")
bar_chart(value, width = width, fill = "red")
},
align = "left"))
)
```
### About
**The Coronavirus Dashboard**
This Coronavirus dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. This dashboard is built with R using the Rmakrdown framework and can easily reproduce by others. The code behind the dashboard available [here](https://github.com/RamiKrispin/coronavirus_dashboard)
**Data**
The input data for this dashboard is the [coronavirus](https://github.com/RamiKrispin/coronavirus) R package (dev version). The data and dashboard is refreshed on a daily bases. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv)
**Packages**
* Dashboard interface - the [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) package.
* Visualization - the [plotly](https://plot.ly/r/) package
* Data manipulation - [dplyr](https://dplyr.tidyverse.org/), and [tidyr](https://tidyr.tidyverse.org/)
* Tables - the [DT](https://rstudio.github.io/DT/) package
**Deployment and reproducibly**
The dashboard was deployed to Github docs. If you wish to deploy and/or modify the dashboard on your Github account, you can apply the following steps:
* Fork the dashboard [repository](https://github.com/RamiKrispin/coronavirus_dashboard), or
* Clone it and push it to your Github package
* Here some general guidance about deployment of flexdashboard on Github page - [link](https://github.com/pbatey/flexdashboard-example)
For any question or feedback, you can either open an [issue](https://github.com/RamiKrispin/coronavirus_dashboard/issues) or contact me on [Twitter](https://twitter.com/Rami_Krispin).
Test
=======================================================================
Row {data-width=400}
-----------------------------------------------------------------------
### Recovery and Death Rates by Country
```{r}
df_summary <-df %>%
# dplyr::filter(country != "Others") %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total_cases = sum(cases)) %>%
tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
dplyr::arrange(- confirmed) %>%
dplyr::filter(confirmed >= 25) %>%
dplyr::select(country, confirmed, recovered, death) %>%
dplyr::mutate(recover_rate = recovered / confirmed,
death_rate = death / confirmed)
df_summary %>%
DT::datatable(rownames = FALSE,
colnames = c("Country", "Confirmed", "Recovered", "Death", "Recovery Rate", "Death Rate"),
options = list(pageLength = nrow(df_summary), dom = 'tip')) %>%
DT::formatPercentage("recover_rate", 2) %>%
DT::formatPercentage("death_rate", 2)
```
Daily Trend
=======================================================================
Column {data-width=400}
-------------------------------------
### New Cases - Top 15 Countries (`r max(df$date)`)
```{r}
max_date <- max(df$date)
df %>%
dplyr::filter(type == "confirmed", date == max_date) %>%
dplyr::group_by(country) %>%
dplyr::summarise(total_cases = sum(cases)) %>%
dplyr::arrange(-total_cases) %>%
dplyr::mutate(country = factor(country, levels = country)) %>%
dplyr::ungroup() %>%
dplyr::top_n(n = 15, wt = total_cases) %>%
plotly::plot_ly(x = ~ country,
y = ~ total_cases,
text = ~ total_cases,
textposition = 'auto',
type = "bar") %>%
plotly::layout(yaxis = list(title = "Number of Cases"),
xaxis = list(title = ""),
margin = list(
l = 10,
r = 10,
b = 10,
t = 10,
pad = 2
))
```
### Daily New Cases - China vs. Rest of the World
```{r}
```
Column {data-width=600}
-------------------------------------
### Recovery and Death Rates for Countries with at Least 20000 Cases
```{r}
df %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total_cases = sum(cases)) %>%
tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
dplyr::arrange(- confirmed) %>%
dplyr::filter(confirmed >= 20000) %>%
dplyr::mutate(recover_rate = recovered / confirmed,
death_rate = death / confirmed) %>%
dplyr::mutate(recover_rate = dplyr::if_else(is.na(recover_rate), 0, recover_rate),
death_rate = dplyr::if_else(is.na(death_rate), 0, death_rate)) %>%
dplyr::ungroup() %>%
dplyr::mutate(confirmed_normal = as.numeric(confirmed) / max(as.numeric(confirmed))) %>%
plotly::plot_ly(y = ~ round(100 * recover_rate, 1),
x = ~ round(100 * death_rate, 1),
size = ~ log(confirmed),
sizes = c(5, 70),
type = 'scatter', mode = 'markers',
color = ~ country,
marker = list(sizemode = 'diameter' , opacity = 0.5),
hoverinfo = 'text',
text = ~paste("", country,
" Confirmed Cases: ", confirmed,
" Recovery Rate: ", paste(round(100 * recover_rate, 1), "%", sep = ""),
" Death Rate: ", paste(round(100 * death_rate, 1), "%", sep = ""))
) %>%
plotly::layout(yaxis = list(title = "Recovery Rate", ticksuffix = "%"),
xaxis = list(title = "Death Rate", ticksuffix = "%",
dtick = 1,
tick0 = 0),
hovermode = "compare")
```
### Cases Status Update for `r max(df$date)`
```{r}
daily_summary <- df %>%
dplyr::filter(date == max(date)) %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(names_from = type, values_from = total) %>%
dplyr::arrange(-confirmed) %>%
dplyr::select(country = country, confirmed, recovered, death)
DT::datatable(data = daily_summary,
rownames = FALSE,
colnames = c("Country", "Confirmed", "Recovered", "Death"),
options = list(pageLength = nrow(daily_summary), dom = 'tip'))
```
Data
=======================================================================
```{r}
df %>%
dplyr::select(Date = date, Province = province, Country = country, `Case Type` = type, `Number of Cases` = cases) %>%
DT::datatable(rownames = FALSE,
options = list(searchHighlight = TRUE,
pageLength = 20), filter = 'top')
```